Problem In Wireless Networks Research Articles

Security-Reliability Tradeoff Analysis of Untrusted Full-Duplex Relay Networks

In this work, we investigate the physical layer security problem of wireless cooperative network, where the communication from a source to a destination is assisted by an untrusted full-duplex amplify-and-forward (AF) relay. In order to realize a positive secrecy rate, cooperative jamming is exploited at the destination. The secrecy outage probability (SOP), connection outage probability (COP), and effective secrecy throughput (EST) are, respectively, derived in closed-form expressions for the fixed gain relaying (FGR) scheme and the variable gain relaying (VGR) scheme. Subsequently, we conduct an asymptotic analysis for both the relaying schemes in the high signal-to-noise ratio (SNR) region to offer valuable insights into practical design. Theory and simulation results demonstrate that the self-interference caused by full-duplex transmission is harmful for the reliability performance, while it is beneficial to the security performance. Moreover, the EST of the considered system increases as the self-interference level decreases, which implies that the self-interference deteriorates an overall performance in terms of the security-reliability tradeoff.

Anomaly Intrusion Detection of Wireless Communication Network-Based on Markov Chain Model

In order to solve the increasingly serious security problems of wireless networks, research on abnormal intrusion detection methods of wireless communication networks based on Markov chain model is proposed. What is usually observed is not the known intrusion behavior but the abnormal phenomenon in the communication process studied, which is completed by detecting the change of system behavior or usage. In this paper, the Markov chain model is used to detect the abnormal intrusion of wireless communication networks. Through the analysis and selection of parameters, the experimental results are ideal, and a variety of judgment methods are compared and analyzed. First, this method can easily distinguish between normal and abnormal data, which reduces the time by about 50% compared with the previous method; Second, the detection result of analysis method 2 is better than that of analysis method 1, and the accuracy is about 20%. The new method proposed in this paper has the characteristics of simple calculation, low algorithm complexity, and easy online detection. This method overcomes the disadvantage that the single-step Markov chain analysis and detection method cannot be strictly established in the nature of the Markov chain, has lower algorithm complexity than the multistep Markov chain analysis and detection method, and is simpler than the parameter calculation of hidden Markov chain model.

Performance Analysis of MAC Layer Protocols for WSN with Considering the Effects of Hidden Node Problem

In this paper, the effects of the hidden node problem in Wireless sensor networks are studied on three different MAC protocols using various field distances and various numbers of nodes. This study provides the best number of nodes to be disseminated in a specific field distance depending on the needed performance metrics. Six performance metrics are used in this study, which are Goodput, Throughput, Packet delivery ratio (PDR), Residual Energy, Average Delay, and the first and last node dead in the network. IEEE 802.11, IEEE 802.15.4, and time division multiple access (TDMA) protocols are used. Results show that TDMA gives the best energy conservation and high delay time with high PDR, while IEEE 802.11 provides the best throughput and goodput results and low delay time.

Multi-group multi-verse optimizer for energy efficient for routing algorithm in wireless sensor network

Wireless sensor network (WSN) is a network composed of a group of wireless sensors with limited energy. With the proliferation of sensor nodes, organization and management of sensor nodes become a challenging task. In this paper, a new topology is proposed to solve the routing problem in wireless sensor networks. Firstly, the sensor nodes are layered to avoid the ring path between cluster heads. Then the nodes of each layer are clustered to facilitate the integration of information and reduce energy dissipation. Moreover, we propose efficient multiverse optimization to mitigate the impact of local optimal solution prematurely and the population diversity declines prematurely. Extensive empirical studies on the CEC 2013 benchmark demonstrate the effectiveness of our new approach. The improved algorithm is further combined with the new topology to handle the routing problem in wireless sensor networks. The energy dissipation generated in routing is significantly lower than that of Multi-Verse Optimizer, Particle Swarm Optimization, and Parallel Particle Swarm Optimization in a wireless sensor network consisting of 5000 nodes.

Dynamic Resource Allocation and Forecast of Snow Tourism Demand Based on Multiobjective Optimization Algorithm.

This paper aims to map the task of reliable transmission of wireless sensor networks. At the same time, this paper transforms the mapping problem of wireless sensor networks into a problem of reducing the energy consumption of mapping under many constraints such as reliability and scheduling length and uses discrete particle swarm optimization algorithm to map. For optimization, the algorithm performs iterative calculations to obtain the best mapping node for each operation so that the inertia coefficient of the existing particle swarm optimization algorithm is improved and linearly minimized with the number of iterations. When resource-demanding tasks need to allocate dynamic resources to multiple nodes to complete collaboratively, adding the mapping principle of the nearest node in the discrete particle swarm optimization mapping reduces the energy consumption of communication between tasks. An in-depth analysis of the influencing factors of the ice and snow tourism market shows that the per capita disposable income of urban residents and the number of urban residents have a significant impact on the ice and snow tourism market demand. In addition, regression analysis and demand-based forecasting are important methods to analyze the scale and development trend of tourism. At the same time, it shows an important position in the purpose of urban tourism and regional market share so as to provide a basis for decision-making in tourism destination marketing. This paper mainly studies and analyzes the wireless sensor network and further introduces it into dynamic resource allocation and ice and snow tourism, which can promote the continuous development of dynamic resource allocation and ice and snow tourism.

An Optimized Distributed Routing Protocol for Energy Management Systems Based on Wireless Sensor Networks in Intelligent and Smart Structures

Energy and spectrum efficiency for energy management systems based on wireless sensor networks in intelligent structures and powered by ambient energy harvesting (EH) are the main problems in wireless sensor networks. Herein, we consider relay selection methods. To address this issue, we proposed the optimal multiantenna power beacon opportunistic relay selection (OMPB-ORS) protocol, which uses decoding and forward methods, in which the relay wireless sensor nodes and the second source are energy-restricted and can harvest energy from a power beacon (PB) multiantenna to transmit aggregated information data from source to destination. The proposed protocol based on specific s witching t ime r eceiver a rchitecture e nhances e nd-to-end p erformance f or m aximum h ardware impairments and interference for the transceiver. To evaluate the performance, we compared our proposed protocol with best ORS (B-ORS), conventional ORS (C-ORS), and hybrid partial relay selection (H-PRS) protocols. Using the Rayleigh-fading channel, the simulation is driven based on asymptotic and exact form expressions of throughput (TP) and outage probability (OP). Simulation results show that the OMPB-ORS protocol achieves a higher TP and OP than all compared protocols.

CFDDR: A Centralized Faulty Data Detection and Recovery Approach for WSN With Faults Identification

One of the most challenging problems in wireless sensor networks (WSNs) is detecting and recovering faulty data. Due to resource limitations, WSNs are subject to many failures, including hardware (permanent faults), software, and communication failures. To ensure data reliability, the data collected by WSNs must be clean of faults. This article presents a centralized faulty data detection and recovery approach, which can detect, recover, and recognize different types of faults (offset, gain, stuck-at, out of bound, and random faults). It operates in two phases: Faulty data detection and recovery and fault type identification (FTI). After detecting the fault, the faulty data will be discarded and replaced by an estimated value based on the Kalman filter. FTI provides a unique solution by determining the types of faults and report them to the end-user for an application-specific decision-making process. The effectiveness of the proposed approach is demonstrated through experimental results, with comparison to state-of-the-art techniques of the same application.

CPAOR: Control Packet Aware On-demand Routing Protocol for WBANs

Background & Objective: The core problem of wireless body area network (WBAN) nodes is their limited energy resource. Thus, an energy-efficient routing protocol is crucial to this type of network. A major drawback of current routing protocols is that they do not consider the monitoring of a large number of patients, which may be problematic in the event of disasters. In addition, most routing algorithms fail to consider balancing energy consumption between nodes. Furthermore, routing overhead (RO) and reduced complexity are required in this type of network. Methods: In this paper, we propose a control packet aware on-demand routing (CPAOR) protocol to overcome the aforementioned drawbacks. The proposed protocol decreases the number of control packets and adds awareness for the transmission of control packets, which can extend the network lifetime. Results and Discussion: We analyze and examine the simulation results of the CPAOR protocol using MATLAB in terms of energy consumption, the energy factor, RO, and changing the number of nodes to more than 60 nodes. The CPAOR protocol demonstrates an improvement of 70.9% compared to the DMQoS, ORACE-Net, and DLQoS protocols in terms of energy consumption. This finding proves that the CPAOR protocol is an efficient routing protocol for WBANs. Conclusion: The CPAOR protocol has been proven to be an effective routing protocol for WBANs.

Game-Theoretic Power and Rate Control in IEEE 802.11p Wireless Networks

Optimization of the transmission power and rate allocation is a significant problem in wireless networks with mobile nodes. Due to mobility, the vehicles establishing wireless networks may exhibit severe fluctuations of their link quality, affecting their connection reliability and throughput. In Vehicular Ad-hoc Networks (VANETS), the IEEE 802.11p standard provides a practical metric for the Packet Reception Ratio (PRR), which is related with the transmission power and rate. Finding a global strategy for optimizing PRR for all mobile nodes can be treated as a potential game where each vehicle is considered as a selfish player, aiming to maximise its transmission reliability while rate constraints are satisfied. To this end, we propose a game-theoretic approach that converges to a Nash equilibrium. The main contributions of this work include: (i) identification of the best case equilibrium, for two cases of interference: diminished or kept stable, and (ii) verification of the equilibrium optimality, by showing that the value of stability is 1. Moreover, numerical results exhibiting the ease of the utility function calculation are provided, especially after an SINR level, whereby the utility function is concave and can be solved efficiently in polynomial time.

A new approach to solving target coverage problem in wireless sensor networks using an effective hybrid genetic algorithm and tabu search

Literature in recent years has introduced several studies conducted to solve the target coverage problem in wireless sensor networks (WSNs). Sensors are conventionally assumed as devices with only a single power level. However, real applications may involve sensors with multiple power levels (i.e., multiple sensing ranges each of which possesses a unique power consumption). Consequently, one of the key problems in WSNs is how to provide a full coverage on all targets distributed in a network containing sensors with multiple power levels and simultaneously prolong the network lifetime as much as possible. This problem is known as Maximum Network Lifetime With Adjustable Ranges (MNLAR) and its NP-completeness has been already proved. To solve this problem, we proposed an efficient hybrid algorithm containing Genetic Algorithm (GA) and Tabu Search (TS) aiming at constructing cover sets that consist of sensors with appropriate sensing ranges to provide a desirable coverage for all the targets in the network. In our hybrid model, GA as a robust global searching algorithm is used for exploration purposes, while TS with its already-proved local searching ability is utilized for exploitation purposes. As a result, the proposed algorithm is capable of creating a balance between intensification and diversification. To solve the MNLR problem in an efficient way, the proposed model was also enriched with an effective encoding method, genetic operators, and neighboring structure. In the present paper, different experiments were performed for the purpose of evaluating how the proposed algorithm performs the tasks defined. The results clearly confirmed the superiority of the proposed algorithm over the greedy-based algorithm and learning automata-based algorithm in terms of extending the network lifetime. Moreover, it was found that the use of multiple power levels altogether caused the extension of the network lifetime.

An ECC-Based Digital Signature Scheme for Privacy Protection in Wireless Communication Network

Aiming at the security problems of wireless communication network and the shortcomings of Elliptic Curve Digital Signature Algorithm (ECDSA), this paper designed a forward secure digital signature scheme and proved that the scheme has forward security and unforgeability in the random oracle model. Experimental simulations are done in this paper, and the results show that the proposed scheme has the highest efficiency compared with the ECDSA scheme and the three existing forward secure schemes. This scheme not only meets the forward security and protects the users’ privacies but also reduces the requirement of computing power of the user in the wireless communication network.

Node localization algorithm for detecting malicious nodes to prevent connection failures and improve end-to-end delay

In sensor network applications, the measured data makes sense only if the location is known. The location of the node is known, and the wireless communication line and transmission method between the two nodes can be an unknown collaboration process where a sensor with a known location is used to locate the node in an unknown location. Various localization algorithms are available that use linear and nonlinear methods to find nodes. Security is a major issue in localization. After the data is sent from the beacon node to the binding node, an error occurs because the virus-safe or unsafe mobile beacon acts like the original mobile beacon, sending the wrong message. One of the basic problems of wireless sensor networks is the localization and security of nodes. This article analyses the performance of optimization algorithms like Ant Colony Optimization algorithms, opportunity routing algorithms, and the proposed Buffalo optimization algorithm. The performance of the buffalo algorithm is compared with these optimization algorithms. Performance metrics such as throughput, residual energy, packet forwarding rate, packet loss rate, delay, and cost were measured. Then the algorithm is implemented as a hardware system for measuring system performance in real-time. Based on a detailed analysis of the performance of optimization algorithms in wireless sensor networks, the buffalo algorithm provides higher efficiency and performance in wireless sensor network applications.

Enabling a MAC Protocol with Self-Localization Function to Solve Hidden and Exposed Terminal Problems in Wireless Ad Hoc Networks

Enabling a MAC Protocol with Self-Localization Function to Solve Hidden and Exposed Terminal Problems in Wireless Ad Hoc Networks

A novel framework for the prevention of black-hole in wireless sensors using hybrid convolution network

Problems of Wireless Sensor Networks (WSN) are associated with a significant increase in the number of devices on these networks. In this regard, the requirements for the protection and the security of WSN from external influences are increasing significantly. WSN security problems are solved by solving the problem of optimal path routing, energy conservation, and so on. This paper proposes a hybrid model of an efficient packet routing and delivery system to prevent Black-hole attacks. This type of attack is considered the most common on the network due to its unique characteristics. To detect such attacks, a deep learning model using a Convolutional Neural Network (CNN) is proposed. The learning algorithm must be reliable and trustworthy so that attack analysis can be considered at different levels to study the intelligent behavior of network attacks. The paper considers the problem of finding the optimal shortest path using Deep Q-Learning and convolutional neural networks to perform efficient routing and delivery of packets in a safer way. As a result of simulation, the achieved accuracy reached 98.57 %.

Detecting Distance-Spoofing Attacks in Arbitrarily-Deployed Wireless Networks

The present work addresses localization problem in arbitrarily-deployed wireless networks in the presence of malicious attackers that can manipulate (spoof) their distance measurements. This problem is very important for the forthcoming applications that depend on accurate location information, since malicious attacks can potentially lead to disastrous consequences without the consideration of the security aspect. Therefore, this work proposes a new scheme for attacker detection based on generalized likelihood ratio test (GLRT) applied after a preliminary location estimate is obtained based on a simple geometric approach. The detected attacker is then removed from the localization process, which is converted into a generalized trust region sub-problem (GTRS) framework by applying the law of cosines (LC) and solved efficiently via bisectioning. The work also introduces theoretical bounds on the probability of detection in closed-form and studies the performance of the proposed algorithm from computational complexity, success in attacker detection, and localization accuracy perspectives. Our simulations corroborate the effectiveness of the proposed scheme, offering good localization accuracy and detection performance even in noisy environments

Location Prediction of Rogue Access Point Based on Deep Neural Network Approach

One of the serious security problems in wireless local networks (WLAN) is the existence of the rogue access points (RAPs). To prevent our network from the RAP attacks, we need to identify the RAPs by using the RAP detection methods. However, the identification of RAP location is also a challenging task. The objective of this paper is to propose the location prediction scheme for the RAP. We call our proposed scheme as the location prediction of rogue access point (LPRAP). The LPRAP scheme consists of two mechanisms, the RAP detection mechanism and the RAP location prediction mechanism. We apply the concept of the fingerprint in the RAP detection mechanism by considering the SSID, time duration of broadcasting beacon frame and MAC address. We show that this mechanism can detect the number of RAP. For the RAP location prediction mechanism, we utilize the deep neuron network (DNN) to predict the location of RAPs and evaluate its effectiveness. We evaluate the performance of LPRAP by comparing with those of other machine learning methods such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes, and Multi-layer Perceptron (MLP). We also compare with particle swarm optimization algorithm. The results show that LPRAP can accurately predict the location of RAP up to 99.29%.

Hybrid algorithm optimization for coverage problem in wireless sensor networks

Hybrid algorithm optimization for coverage problem in wireless sensor networks

When Optimization Meets Machine Learning: The Case of IRS-Assisted Wireless Networks

Performance optimization of wireless networks is typically complicated because of high computational complexity and dynamic channel conditions. Considering a specific case, the recent introduction of intelligent reflecting surface (IRS) can reshape the wireless channels by controlling the scattering elements' phase shifts, namely, passive beamforming. However, due to the large size of scattering elements, the IRS's beamforming optimization becomes intractable. In this article, we focus on machine learning (ML) approaches for complex optimization problems in wireless networks. ML approaches can provide flexibility and robustness against uncertain and dynamic systems. However, practical challenges still remain due to slow convergence in offline training or online learning. This motivated us to design a novel optimization-driven ML framework that exploits the efficiency of model-based optimization and the robustness of model-free ML approaches. Splitting the control variables into two parts allows one part to be updated by the outer loop ML approach while the other part is solved by the inner loop optimization. The case study in IRS-assisted wireless networks confirms that the optimization-driven ML framework can improve learning efficiency and the reward performance significantly compared to conventional model-free ML approaches.

CCAIB: Congestion Control Based on Adaptive Integral Backstepping for Wireless Multi-Router Network.

Wireless information collecting and processing terminals, such as cell phones, sensors and smart wearable devices, are expected to be deployed on a large scale in the future to promote the continuous advancement of the global information revolution. Since most of these terminals connect to each other using long-distance and high-speed networks by multiple routers and eventual access the internet, the application of mobile internet is gradually increasing and data traffic on the mobile internet is growing exponentially, from which arises congestion in wireless networks on multiple routers. This research solves the congestion problem for wireless networks with multiple bottleneck routers. First, the wireless network model is expanded to multi-router networks, which considers the interrelationships between connecting routers. Afterwards, a new Active Queue Management (AQM) method called Congestion Control Based on Adaptive Integral Backstepping (CCAIB) is designed to handle congestion in wireless networks. In CCAIB, an adaptive control method is used to estimate the packet loss ratios of wireless links and a controller is designed based on the estimation results through a backstepping procedure. It can be shown from the simulation results that the performance of CCAIB is better than the H∞ algorithm in queue length stability. Besides, the window size of CCAIB is 100 times that of the H∞ algorithm, and the proportion of packets marked as discarded when using CCAIB is about 0.1% of the H∞ algorithm. Moreover, CCAIB has satisfactory adaptability to network parameters such as wireless link capacity, propagation delay, wireless packet loss ratios, desired queue length and router location.

Energy efficient architecture for mitigating the hot-spot problem in wireless sensor networks

Energy efficient architecture for mitigating the hot-spot problem in wireless sensor networks